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On-line Algorithm for Blind Signal Extraction of Arbitrarily Distributed, but Temporally Correlated Sources Using Second Order Statistics

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Abstract

Most of the algorithms for blind separation/extraction and independent component analysis (ICA) can not separate mixtures of sources with extremely low kurtosis or colored Gaussian sources. Moreover, to separate mixtures of super- and sub-Gaussian signals, it is necessary to use adaptive (time-variable) or switching nonlinearities which are controlled via computationally intensive measures, such as estimation of the sign of kurtosis of extracted signals. In this paper, we develop a very simple neural network model and an efficient on-line adaptive algorithm that sequentially extract temporally correlated sources with arbitrary distributions, including colored Gaussian sources and sources with extremely low values (or even zero) of kurtosis. The validity and performance of the algorithm have been confirmed by extensive computer simulation experiments.

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Authors and Affiliations

  1. Laboratory for Open Information Systems, Brain Science Institute, RIKEN, 2-1 Hirosawa, Wako-shi, Saitama, 351-0198, Japan

    Andrzej Cichocki

  2. Warsaw University of Technology, 00-661, Warsaw, Poland

    Andrzej Cichocki

  3. Department of Information Systems Engineering, Kochi University of Technology, 185 Miyanokuchi, Tosayamada-cho, Kami-gun, Kochi, 782-8502, Japan

    Ruck Thawonmas

Authors
  1. Andrzej Cichocki
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  2. Ruck Thawonmas
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Cichocki, A., Thawonmas, R. On-line Algorithm for Blind Signal Extraction of Arbitrarily Distributed, but Temporally Correlated Sources Using Second Order Statistics. Neural Processing Letters 12, 91–98 (2000). https://doi.org/10.1023/A:1009616029367

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  • DOI: https://doi.org/10.1023/A:1009616029367

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